Abstract
Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy and computation times which allows to assess some of the strengths and limitations of both approaches. We also present a novel method to approximate length regularity in a graph cut based framework: Instead of using pairwise terms we introduce higher order terms. These allow to represent a more accurate discretization of the L 2-norm in the length term.
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Klodt, M., Schoenemann, T., Kolev, K., Schikora, M., Cremers, D. (2008). An Experimental Comparison of Discrete and Continuous Shape Optimization Methods. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_26
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DOI: https://doi.org/10.1007/978-3-540-88682-2_26
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